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# Lights, Camera…
With the launch of Veo and Sora, video generation has reached a new high. Creators are experimenting extensively, and teams are integrating these tools into their marketing workflows. However, there is a drawback: most closed systems collect your data and apply visible or invisible watermarks that label outputs as AI-generated. If you value privacy, control, and on-device workflows, open source models are your best option, and several now rival the results of Veo.
In this article, we will review the top five video generation models, providing technical knowledge and a demo video to help you assess their video generation capabilities. Every model is available on Hugging Face and can run locally via ComfyUI or your preferred desktop AI applications.
# 1. Wan 2.2 A14B
Wan 2.2 upgrades its diffusion backbone with a Mixture-of-Experts (MoE) architecture that splits denoising across timesteps into specialized experts, increasing effective capacity without a compute penalty. The team also curated aesthetic labels (e.g. lighting, composition, contrast, color tone) to make “cinematic” looks more controllable. Compared to Wan 2.1, training scaled substantially (+65.6% images, +83.2% videos), improving motion, semantics, and aesthetics.
Wan 2.2 reports top-tier performance among both open and closed systems. You can explore the text-to-video and image-to-video A14B repositories on Hugging Face: Wan-AI/Wan2.2-T2V-A14B and Wan-AI/Wan2.2-I2V-A14B
# 2. Hunyuan Video
HunyuanVideo is a 13B-parameter open video foundation model trained in a spatial–temporal latent space via a causal 3D variational autoencoder (VAE). Its transformer uses a “dual-stream to single-stream” design: text and video tokens are first processed independently with full attention and then fused, while a decoder-only multimodal LLM serves as the text encoder to improve instruction following and detail capture.
The open source ecosystem includes code, weights, single- and multi-GPU inference (xDiT), FP8 weights, Diffusers and ComfyUI integrations, a Gradio demo, and the Penguin Video Benchmark.
# 3. Mochi 1
Mochi 1 is a 10B Asymmetric Diffusion Transformer (AsymmDiT) trained from scratch, released under Apache 2.0. It couples with an Asymmetric VAE that compresses videos 8×8 spatially and 6x temporally into a 12-channel latent, prioritizing visual capacity over text while using a single T5-XXL encoder.
In preliminary evaluations, the Genmo team positions Mochi 1 as a state-of-the-art open model with high-fidelity motion and strong prompt adherence, aiming to close the gap with closed systems.
# 4. LTX Video
LTX-Video is a DiT-based (Diffusion Transformer) image-to-video generator built for speed: it produces 30 fps videos at 1216×704 faster than real time, trained on a large, diverse dataset to balance motion and visual quality.
The lineup spans multiple variants: 13B dev, 13B distilled, 2B distilled, and FP8 quantized builds, plus spatial and temporal upscalers and ready-to-use ComfyUI workflows. If you are optimizing for fast iterations and crisp motion from a single image or short conditioning sequence, LTX is a compelling choice.
# 5. CogVideoX-5B
CogVideoX-5B is the higher-fidelity sibling to the 2B baseline, trained in bfloat16 and recommended to run in bfloat16. It generates 6-second clips at 8 fps with a fixed 720×480 resolution and supports English prompts up to 226 tokens.
The model’s documentation shows expected Video Random Access Memory (VRAM) for single- and multi-GPU inference, typical runtimes (e.g. around 90 seconds for 50 steps on a single H100), and how Diffusers optimizations like CPU offload and VAE tiling/slicing affect memory and speed.
# Choosing a Video Generation Model
Here are some high-level takeaways for helping choose the right video generation model for your needs.
- If you want cinema-friendly looks and 720p/24 on a single 4090: Wan 2.2 (A14B for core tasks; the 5B hybrid TI2V for efficient 720p/24)
- If you need a large, general-purpose T2V/I2V foundation with strong motion and a full open source software (OSS) toolchain: HunyuanVideo (13B, xDiT parallelism, FP8 weights, Diffusers/ComfyUI)
- If you want a permissive, hackable state-of-the-art (SOTA) preview with modern motion and a clear research roadmap: Mochi 1 (10B AsymmDiT + AsymmVAE, Apache 2.0)
- If you care about real-time I2V and editability with upscalers and ComfyUI workflows: LTX-Video (30 fps at 1216×704, multiple 13B/2B and FP8 variants)
- If you need efficient 6s 720×480 T2V, solid Diffusers support, and quantization down to small VRAM: CogVideoX-5B
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
